Q: How is a specific line of business / business unit using your predictive decisions? How is your product deployed into operations?

A: My solutions have been deployed in a variety of settings including marketing and HR. In some situations, these solutions are deployed into an automated production environment. In other areas, reports can be refreshed on an ad hoc basis. But in both situations end-users receive a report that helps them make intelligent business decisions.

Q: If HR were 100% ready and the data were available, what would your boldest data science creations do?

A: I think you have to start simple. The tendency I’ve noticed is to over-complicate things. If HR were 100% ready (and at some companies HR is ready in this way) I would look to marry together different sources of data and build solutions that offer value for companies (e.g. people analytics, compensation analytics).

Q: When do you think businesses will be ready for “black box” workforce predictive methods, such as Random Forests or Neural Networks?

A: I think businesses are ready now. The argument that a random forest or neural network is more “black box” than a logit (for instance) is somewhat overstated in my opinion. For example, I think logit coefficients are confusing for most audiences and I always represent these estimates as changes in probabilities. A random forest or neural network is more challenging to interpret but in general you can create the same insights (e.g. predicted probability for a binary outcome).

Q: Do you have suggestions for data scientists trying to explain the complexity of their work, to those solving workforce challenges?

A: Focus on simple insights because business users don’t care about the complexity of your method, they care about the value your work brings to the company. So, for example, whether a data scientist is using a logit or random forest they should always produce simple graphics or tables that illustrate how businesses can improve their bottom line or the state of their workforce.

Q: What is one specific way in which predictive analytics actively is driving decisions?

A: Quite a few businesses already use churn models to understand why their employees leave. As an extension, some companies use predictive analytics to prevent the churn of top performers.